Bandwidth Selection in Pre-Smoothed Particle Filters

32 Pages Posted: 16 Jun 2012

Date Written: June 15, 2012


This paper introduces the Pre-Smoothed Particle Filter (PSPF) as a regularized alternative to the Sampling Importance Resampling (SIR) filter for non-linear state space models with additive Gaussian observation noise. The PSPF adaptively bridges the unbiased, but variance-prone, SIR filter and the biased, but less variance-prone Gaussian update filter. Our main contribution is the adaptive determination of the degree of smoothing in each time step. We illustrate the approach using examples from econometrics, namely a continuous time short term interest rate model and a dynamic stochastic general equilibrium model fitted to noisy data. The PSPF is shown to be highly suited for dynamic models with high signal-to-noise ratio, for which the SIR filter has problems.

Keywords: Dynamic Stochastic general equilibrium, Particle filter, Smoothing

JEL Classification: C15

Suggested Citation

Kleppe, Tore Selland and Skaug, Hans, Bandwidth Selection in Pre-Smoothed Particle Filters (June 15, 2012). Available at SSRN: or

Tore Selland Kleppe (Contact Author)

University of Stavanger ( email )

PB 8002
Stavanger, 4036

Hans Skaug

University of Bergen ( email )

Muséplassen 1
N-5008 Bergen, +47 55 58

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